nf-core-crispriscreen

nf-core/crispriscreen: Output

Introduction

This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.

The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory.

Pipeline overview

The pipeline is built using Nextflow and processes data using the following steps:

1. Sub-sampling of reads (Seqtk/sample, optional)
2. Read QC (FastQC)
3. Generic adapter and quality trimming (Trim Galore!)
4. Specific primer sequence trimming (cutadapt)
5. Preparation of *.fasta library (custom R script)
6. Alignment using (Bowtie2)
   1. Build index from *.fasta library
   2. Align reads to library
   3. Optional filtering by mapping quality
7. Count reads per target and input file (subread/featurecounts)
8. Quantify gene fitness score from multiple targets per gene
   1. Option 1: Gene fitness is calculated using Mageck MLE
   2. Option 2: Gene fitness is calculated using DESeq2
9. Generate HTML report with fitness results (R markdown)
10. Present QC for raw and mapped reads (MultiQC)

Seqtk/Sample

Output files

• seqtk/
  • *.subsampled.fastq.gz: Subsampled compressed fastq.gz sequencing reads.

Sub-sampling of reads (Seqtk/sample, optional).

FastQC

Output files

• fastqc/
  • *_fastqc.html: FastQC report containing quality metrics.
  • *_fastqc.zip: Zip archive containing the FastQC report, tab-delimited data file and plot images.

FastQC gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the FastQC help pages.

:::note The FastQC plots displayed in the MultiQC report shows untrimmed reads. They may contain adapter sequence and potentially regions with low quality. :::

Trimgalore

Output files

• trimgalore/
  • *_trimming_report.txt: Report of the trimming results for each *.fastq.gz input file.

Generic adapter (e.g. Illumina) and quality trimming of reads (Trim Galore!).

Cutadapt

Output files

• cutadapt/
  • *_cutadapt.log: Report of the trimming results for each *.fastq.gz input file.
  • *.trim.fastq.gz: Files with trimmed reads.

Specific primer sequence trimming (cutadapt).

Preparation of *.fasta library

Output files

• prepare/
  • *.saf: The input library in saf format converted from the provided .fasta file.
  • *_controls.tsv: Optional table in tab-separated format with overview of control barcodes.

This module generates a .tsv input table from the provided .fasta file. It also checks if a pattern for control barcodes has been passed with the --gene_controls parameter. In case a pattern has been supplied but no matching barcodes were found, it stops with an error.

Bowtie2

Output files

• bowtie2/bowtie2/
  • *.bt2: Bowtie2 index created from the libary *.fasta file.
• bowtie2/
  • *.bam: Compressed sequence alignment files, one per input .fastq.gz.
  • *.bowtie2.log: Bowtie2 log file, one per input .fastq.gz.
  • *.unmapped.fastq.gz: Optionally exported unmapped reads, one per input .fastq.gz.

Bowtie2 is used for mapping reads to the ‘genome’, here the library of guide RNAs/barcodes.

FeatureCounts

Output files

• subread/
  • *.txt: Main result of this module, a table with detailed read counts per target (guide RNA/barcode)
  • *.txt.summary: Summary of mapped and unmapped reads.

Summarizes read counts per target and input file, see subread/featurecounts. This is the input for fitness score calculation with DESeq2.

Mageck

Output files

• mageck/
  • all_counts.gene_summary.txt: Table with beta score, z score, p-value and false discovery rate for all targeted genes.
  • all_counts.sgrna_summary.txt: Table with overview of sgRNA-gene association and sgRNA efficiency.

DESeq2

Output files

• fitness/
  • all_counts.tsv: A summary table with all read counts per target (gene, barcode, sgRNA, …), concatenated from the individual [#featurecounts] output files.
  • result.tsv: Table with fitness scores and other statistics for all conditions in *.tsv format.
  • result.Rdata: Table with fitness scores and other statistics for all conditions in memory-efficient *.Rdata format. Can be read into R using load("result.Rdata).

A custom R script employing DESeq2 to quantify gene fitness score from multiple targets per gene, reporting different summary statistics. The final output of this module is a table in *.txt and *.Rdata format with the following columns:

Column	Type	Example	Comment
sgRNA	chr	aat_111	name of sgRNA as in .fasta reference
sgRNA_target	chr	aat	name of sgRNA target gene
sgRNA_position	numeric	111	position of sgRNA relative to target start
condition	chr	example	experimental condition
date	chr	2021_01_09	experiment date
time	numeric	0	time / n generations, important for fitness calculation
group	numeric	1	group number for sample
reference_group	numeric	1	group number of reference for comparison
baseMean	numeric	NA	DESeq2 average number of reads for sgRNA
log2FoldChange	numeric	0	DESeq2 log2 FC for sgRNA
lfcSE	numeric	0	DESeq2 log2 FC error for sgRNA
stat	numeric	NA	DESeq2 t statistic for sgRNA
pvalue	numeric	1	DESeq2 p-value for sgRNA
padj	numeric	1	DESeq2 adjusted p-value for sgRNA
fitness	numeric	2.020183	fitness for sgRNA
sgRNA_index	numeric	4	relative position of sgRNA
sgRNA_correlation	numeric	0.6247412	correlation of sgRNA with others
sgRNA_efficiency	numeric	0.9893041	relative repression efficiency of sgRNA
wmean_log2FoldChange	numeric	0	weighted mean log2 FC for gene
sd_log2FoldChange	numeric	0	standard deviation of log2 FC for gene
wmean_fitness	numeric	1.777574	weighted mean fitness for gene
sd_fitness	numeric	0.9558989	standard dev of fitness for gene
p_fitness	numeric	0.001	p-value from Wilcoxon rank sum test (Null: fitness ~ 0)
p_fitness_adj	numeric	0.0001	p-value from Wilcoxon test, Benjamini-Hochberg adjusted
comb_score	numeric	0.0001	combined score (-log10(p-value) * abs(wmean_fitness)

R markdown report

Output files

• fitness_report/
  • counts_summary.nb.html: HTML report with information about all samples and read counts.
  • fitness_summary.nb.html: HTML report with information about fitness scores for all conditions.
  • svg/png/pdf/: sub folders with exported figures in SVG, PNG and PDF format
  • csv: sub folder with additional tables created duing the reporting stage

Custom R markdown templates are used to render two HTML reports with information about all samples, their number of mapped reads, barcodes, genes, fitness scores, and other information. If fitness calculation is omitted through the option gene_fitness = false in the call to nextflow run ..., only the first report is written. The same applies for having less than two time points in the samplesheet.csv table.

MultiQC

Output files

• multiqc/
  • multiqc_report.html: a standalone HTML file that can be viewed in your web browser.
  • multiqc_data/: directory containing parsed statistics from the different tools used in the pipeline.
  • multiqc_plots/: directory containing static images from the report in various formats.

MultiQC is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.

Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see http://multiqc.info.

Pipeline information

Output files

• pipeline_info/
  • Reports generated by Nextflow: execution_report.html, execution_timeline.html, execution_trace.txt and pipeline_dag.dot/pipeline_dag.svg.
  • Reports generated by the pipeline: pipeline_report.html, pipeline_report.txt and software_versions.yml. The pipeline_report* files will only be present if the --email / --email_on_fail parameter’s are used when running the pipeline.
  • Reformatted samplesheet files used as input to the pipeline: samplesheet.valid.csv.
  • Parameters used by the pipeline run: params.json.

Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.

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